As a quality professional, you already have a deluge of critical product and process data to collect, analyze and present as actionable intelligence. Add gauge repeatability and reproducibility (R and R) studies to your list of responsibilities, and it is no wonder you find yourself overwhelmed at times. To refresh your memory of key elements of gauge R and R studies, this blog post reiterates what you need to know before you begin your next study.
What is a gauge R and R study?
In short, gauge R&R studies give you a means to measure variables that affect measurement processes. Essentially, the two main variables you are testing are repeatability and reproducibility. Since these two variables coincide with one another very closely, here are basic definitions of each one.
From the perspective of Six Sigma methodology, you can think of repeatability as a form of variation that occurs when an operator (i.e., appraisers of measurement processes during an R&R study) measures a particular measurement process, or item, across several trials. To determine repeatability as accurately as possible, the operator must use identical measuring equipment for each trial, thus giving you a large enough sample set to move on to the reproducibility side of the coin.
When you calculate variance for the average measurements of all operators in a gauge R&R study, you are calculating reproducibility. It is important to remember that all operators in the study must use identical measuring equipment for the purposes of calculating reproducibility as accurately as possible. As such, you can also utilize reproducibility processes and methods in gauge R&R studies that analyze data across different facilities.
Why are gauge R&R studies so important?
To place the importance of gauge R&R studies in layman’s terms, these analyses may be your only opportunity to study the measurement processes you rely on every day. It is also important to realize that conducting a study of measurement processes does not necessarily mean that you need to measure each and every gauge on the shop floor at every facility. Depending on how you want to optimize the study, you may only need to analyze the measurement process itself. Read more about the IQS software for gauge R and R studies.
What is the best way to complete gauge R&R studies?
The simplicity of the previous definitions belies the complexity of a properly executed gauge R&R study. If you operate in a highly regulated industry, such as aerospace and defense or medical device manufacturing, you already know how challenging it is to endure an audit of your gauge R&R studies. Regulations that govern measurement systems demand accountability, and as a quality professional, you have to be able to show auditors that your company is thoroughly fulfilling these requirements.
What you may have forgotten when it comes to gauge R&R studies is the fact that controversy exists as to which approach is most accurate. The three basic approaches to R&R study data are:
- The analysis of variance (ANOVA) approach.
- The Automotive Industry Action Group’s approach.
- Evaluating the measurement process (EMP) approach.
Interestingly, some experts in the field have criticized AIAG’s approach over the years. Published in Quality Digest a few years ago, Dr. Donald Wheeler wrote that “the AIAG approach simply overstates the damage due to measurement error and condemns the measurement process. Both the ANOVA approach and the EMP approach show this measurement system to have very good utility for measuring this product.”
The most important takeaway from this refresh of gauge R&R studies is the fact that they are not necessarily easy to execute. Your task moving forward is to contribute to the discussion to lift quality to new heights.
This article originally appeared here.